1. Technical Field of the Present Disclosure
The present disclosure relates generally to the field of seizure identification and more particularly to the field of identifying seizures by monitoring changes in heart rates.
2. Background of the Present Disclosure
Seizures are characterized by abnormal or excessive neural activity in the brain. Seizures may involve loss of consciousness or awareness, and result in falls, uncontrollable convulsions, etc. Significant injuries may result not only from the neuronal activity in the brain but also from the associated loss of motor function from falls or the inability of the patient to perceive and/or respond appropriately to potential danger or harm.
It is desirable to identify a seizure event as quickly as possible after the beginning of the seizure, to allow appropriate responsive action to be taken. Such actions may include sending an alert signal to the patient or a caregiver, taking remedial action such as making the patient and/or the immediate environment safe (e.g., terminating operation of equipment, sitting or lying down, moving away from known hazards), initiating a treatment therapy, etc. Where rapid detection is not possible or feasible, it is still desirable to be able to identify seizures after they have begun to allow a physician and/or caregiver to assess the patient's condition and determine whether existing therapies are effective or require modification and/or additional therapy modalities (for example, changing or adding additional drug therapies or adding a neurostimulation therapy). Seizure detection algorithms have been proposed using a variety of body parameters, including brain waves (e.g., electroencephalogram or EEG signals), heart beats (e.g., electrocardiogram or EKG), and movements (e.g., triaxial accelerometer signals). See, e.g., U.S. Pat. No. 5,928,272 and U.S. application Ser. No. 12/770,562, both of which are hereby incorporated by reference herein.
Detection of seizures using heart data requires that the seizure detection algorithm distinguish—or attempt to distinguish—between pathological changes in the detected heart signal (which may indicate a seizure) and non-pathological changes that may be similar to pathological changes but involve normal physiological functioning. For example, the patient's heart rate may increase both when a seizure event occurs and when the patient exercises, climbs stairs or performs other physiologically demanding acts. In some instances, state changes such as rising from a prone or sitting position to a standing position, such as in rising after a sleep period, may produce cardiac changes similar to seizure events. Thus, seizure detection algorithms must distinguish between changes in heart rate due to a seizure and those due to exertional or positional/postural changes.
Current algorithms fail to provide rapid and accurate detection. There is a need for improved algorithms that can more accurately distinguish between ictal and non-ictal heart rate changes. There is also a need for algorithms that may provide an initial detection to allow early warning or therapeutic intervention, and which allows for continued signal analysis subsequent to the initial detection, and permitting the initial detection to be subsequently confirmed or rejected as a seizure based on the signal data acquired after the initial detection. The present invention addresses limitations associated with existing cardiac-based seizure detection algorithms.